With popularization of the digital cameras, the digital images occupy more and more important position in manufacture and life. Especially, in manufacture automation, the digital images have important functions on target identification and target trace, etc. However, since the defects of the imaging technology itself, qualities of the digital images are affected such that the applications of digital images are restricted.
In real life, the brightness dynamic scope is very large, mainly affected by environment illumination, there is a difference of several orders of magnitude between the brightness under the direct irradiation of the sun and the brightness in the shadow. The dynamic scope of the digital cameras is much less relatively, and the most often used 8-bits image depth only represents 256 brightness orders. In different illumination conditions, the vision systems of human may remove influence of the illumination by the adjustment of the size of pupil and process of retina and cortex of cerebra, to identify an object correctly. However, cameras do not possess such a self-regulating function. Therefore, in a case that the illumination condition is bad (too dark or too bright), the interested objects can not be identified on the images, such that the quality of images is deteriorated greatly.
General methods for solving this problem are gray scale equalization or Gamma correction. However, these two processing methods both are the global processing methods, and the local information is ignored. Therefore, although the illuminations are improved after enhancing the image by the above methods, the local image details may be lost. Comparatively, the present invention is based on the Retinex model, and removes the influence of the illumination from the input image by decomposing the input image into an illumination image and a reflection image, so that it can improve illumination effects in the output images, and meanwhile protects the local image details in the input image well.
After searching the literature of the prior arts, it was found that an article of “A Variational Framework for Retinex” in “International Journal of Computer Vision” (page 7-23, vol. 1, 52, in 2003) by Ron. Kimmel, Michael Elad, etc. It provided an image enhancing system and method based on the Retinex model, specifically, firstly collecting an input image and then decomposing the input image into an illumination image and a reflection image. This image decomposing method is completed by the following manners: according to the Retinex model, any image can be decomposed into a product of the illumination image and the reflection image. The core of image decomposing is the estimation of the illumination image, i.e., the forecast of the environment illumination. Based on three restrictions mentioned in the Retinex variational model: the illumination image is smooth in the space field, a pixel value of the illumination image is larger than a pixel value of the input image, and the illumination image and the input image are close enough, the forecast of the environment illumination estimates the environment illumination components, obtains a very smooth image as a forecast of the illumination image, and then obtains the reflection image from the relationships between the input image and the illumination image, the reflection image. After the image is decomposed into the illumination image and the reflection image, the illumination image of the input image is processed separately. The visibility of regions with bad illumination in original images, and the quality of images are improved by the non-linear correction (such as: processes of Gamma correction, gray scale equalization, logarithmic transformation, exponential transformation, subsection linear mapping, etc) for the pixel values of the illumination image according to the application requirements. The attached FIG. 1 shows a schematic block diagram of the image enhancing system in the “Variational Framework for Retinex”.
The shortages of the above system and method are: although the illumination effect in the input image can be improved, the noises in the input image are improved while the image details contents are improved. Therefore, for the input image including a lot of noises originally, the quality of the output image may be worse than that of the input image. The influence of the noises on the quality of output image can not be avoided while enhancing the image details.